XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning

TL;DR

XGBOD is a semi-supervised ensemble method that enhances outlier detection by combining unsupervised representation learning with supervised classification, outperforming existing methods on multiple datasets.

Contribution

The paper introduces XGBOD, a hybrid ensemble algorithm that integrates unsupervised feature extraction with supervised outlier detection, improving accuracy over prior approaches.

Findings

XGBOD outperforms individual detectors and existing algorithms.

The hybrid approach achieves superior detection accuracy.

Results validated across seven diverse datasets.

Abstract

A new semi-supervised ensemble algorithm called XGBOD (Extreme Gradient Boosting Outlier Detection) is proposed, described and demonstrated for the enhanced detection of outliers from normal observations in various practical datasets. The proposed framework combines the strengths of both supervised and unsupervised machine learning methods by creating a hybrid approach that exploits each of their individual performance capabilities in outlier detection. XGBOD uses multiple unsupervised outlier mining algorithms to extract useful representations from the underlying data that augment the predictive capabilities of an embedded supervised classifier on an improved feature space. The novel approach is shown to provide superior performance in comparison to competing individual detectors, the full ensemble and two existing representation learning based algorithms across seven outlier datasets.